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{"references": ["T. S. Rappaport, Wireless Communications, Principles and Practice,\nPrentice Hall, New Jersey, 1996.", "M. K. Varanasi and B. Aazhang, \"Multistage detection in asynchronous\ncode-division multiple-access communications,\" IEEE Trans. Commun.,\nvol. 38, no. 4, pp. 509-519, Apr. 1990.", "P. Patel and J. Holtzman, \"Analysis of a simple successive interference\ncancellation scheme in a DS/CDMA system,\" IEEE J. Select. Areas\nCommun., vol. 12, no. 4, pp. 796-806, June 1994.", "S.Moshavi,\"Multi-user detection for DS-CDMA communications,\"\nIEEE Commun. Mag., pp. 124-136, Oct. 1996.", "J. G. Proakis, Digital Communications, 4th ed. New York: McGraw-\nHill, 2001.", "Heng Siong Lim and B. Venkatesh, \"An Effective Memetic Algorithm\nfor the Optimum Multiuser Detection Problem\",ISSSTA2004, Sydney,\nAustralia, 30 Aug. - 2 Sep. 2004.", "C. Erg\u252c\u00bfun and K. Hacioglu, \"Multiuser detection using a genetic\nalgorithm in CDMA communications systems,\" IEEE Trans. Commun.,\nVol. 48, No. 8, pp. 1374-1383, Aug. 2000.", "H. S. Lim, M. V. C. Rao, W. C. Tan and H. T. Chuah, \"Multiuser\ndetection for DS-CDMA systems using evolutionary programming,\"\nIEEE Commun. Lett., Vol. 7 Issue:3, Mar. 2003.", "A. AlRustamani and B. R. Vojcic, \"A new approach to greedy multiuser\ndetection,\" IEEE Trans. Commun., Vol. 50(8), pp. 1326-1336, Aug.\n2002.\n[10] L. Wei, L. K. Rasmussen, and R. Wyrwas, \"Near optimum tree-search\ndetection schemes for bit-synchronous multiuser CDMA systems over\nGaussian and two-path Rayleigh-fading channels,\" IEEE Trans.\nCommun., vol. 45, pp. 691-700, June 1997.\n[11] Kai Yen and Lajos Hanzo, \"Genetic Algorithm Assisted Joint Multiuser\nSymbol Detection and Fading Channel Estimation for Synchronous\nCDMA Systems\" IEEE journal on selected areas in communications,\nvol. 19, no. 6, june 2001.\n[12] P. H. Tan and L. K. Rasmussen, \"Multiuser detection in CDMA - a\ncomparison of relaxation, exact, and heuristic search methods,\" IEEE\nTrans. Wireless Commun., vol. 3 no. 5, pp 1802-1809 sep. 2004.\n[13] M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA:\nMIT Press, 1996.\n[14] C. Sengupta, J. R. Cavallaro, and B. Aazhang, \"On multipath channel\nestimation for CDMA using multiple sensors,\" IEEE Trans. Commun.,\nvol. 49, pp. 543-553, Mar. 2001.\n[15] A. M. Sayeed , A. Sendonaris and B. Aazhang \"Multiuser Detection in\nFast fading multipath environments\", IEEE journal on selected areas in\ncommunications, vol. 16, no. 9, june 1998.\n[16] A. Sayeed and B. Aazhang, Joint Multipath-Doppler Diversity in Mobile\nWireless Communications, IEEE Transactions on Communications, pp.\n123-132, January 1999."]}
In this paper, a simple heuristic genetic algorithm is used for Multistage Multiuser detection in fast fading environments. Multipath channels, multiple access interference (MAI) and near far effect cause the performance of the conventional detector to degrade. Heuristic Genetic algorithms, a rapidly growing area of artificial intelligence, uses evolutionary programming for initial search, which not only helps to converge the solution towards near optimal performance efficiently but also at a very low complexity as compared with optimal detector. This holds true for Additive White Gaussian Noise (AWGN) and multipath fading channels. Experimental results are presented to show the superior performance of the proposed techque over the existing methods.
SuccessiveInterference Cancellation., Genetic Algorithm (GA), Multistage Detectors (MSD), Multiple AccessInterference (MAI)
SuccessiveInterference Cancellation., Genetic Algorithm (GA), Multistage Detectors (MSD), Multiple AccessInterference (MAI)
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